Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158080
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dc.contributor.authorLi, Dichenen_US
dc.date.accessioned2022-05-29T08:08:39Z-
dc.date.available2022-05-29T08:08:39Z-
dc.date.issued2022-
dc.identifier.citationLi, D. (2022). Occluded person re-identification. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158080en_US
dc.identifier.urihttps://hdl.handle.net/10356/158080-
dc.description.abstractPerson re-identification (person Re-ID) is defined as a matching process of a group of target people across different disjoint cameras. It can be applied to many public occasions for security use such as in the surveillance system of shopping centers. With the advancements in technology, person Re-ID has seen improvements over the last few years. However, the extreme assumption of full-body images is too limited for real-world applications. Existing person Re-ID algorithms face a serious performance drop in matching accuracy with the datasets with occluded images. This project focused on the occluded person re-identification task. After conducting a comprehensive survey of the existing public datasets and the state-of-the-art (SOTA) methods for the occluded person Re-ID problem, we categorized the existing methods into human key-point matching methods such as Pose-Guided Feature Alignment (PGFA) and attention-based methods such as Attention Framework of Person Body (AFPB). In this project, we have implemented several methods and conducted a comprehensive performance comparison using several benchmarks datasets such as, Occluded_REID, Partial_REID and Occluded_Duke. We evaluated the advantage and limitations of those methods. Finally, the findings of this project offer us some inspiration for the potential improvement in the future.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleOccluded person re-identificationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorAlex Chichung Koten_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.researchRapid-Rich Object Search (ROSE) Laben_US
dc.contributor.supervisoremailEACKOT@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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